Quantitative Results
Statistical Analysis
Symbols Used in an APA-Style Regression Table
Source | B | SE B | β | t | p |
Variable 1 | 1.57 | 0.23 | .23 | 2.39 | .020 |
Variable 2 | 1.26 | 2.26 | .05 | 0.58 | .560 |
Variable 3 | -1.65 | 0.17 | -.28 | 2.92 | .005 |
There are five symbols that easily confuse students in a regression table: the unstandardized beta (B), the standard error for the unstandardized beta (SE B), the standardized beta (β), thettest statistic (t), and the probability value (p). Typically, the only two values examined are theBand thep. However, all of them are useful to know.
The first symbol in the regression table is the unstandardized beta (B). This value represents the slope of the line between the predictor variable and the dependent variable. So for Variable 1, this would mean that for every one unit increase in Variable 1, the dependent variable increases by 1.57 units. Also similarly, for Variable 3, for every one unit increase in Variable 3, the dependent variabledecreasesby 1.65 units.
The next symbol is the standard error for the unstandardized beta (SE B). This value is similar to the standard deviation for a mean. The larger the number, the more spread out the points are from the regression line. The more spread out the numbers are, the less likely that significance will be found.
The third symbol is the standardized beta (β). This works very similarly to acorrelation coefficient. It will range from 0 to 1 or 0 to -1, depending on the direction of the relationship. The closer the value is to 1 or -1, the stronger the relationship. With this symbol, you can actually compare the variables to see which had the strongest relationship with the dependent variable, since all of them are on the 0 to 1 scale. In the table above, Variable 3 had the strongest relationship.
The fourth symbol is thettest statistic (t). This is the test statistic calculated for the individual predictor variable. This is used to calculate thepvalue.
The last symbol in the regression table is theprobabilitylevel (p). This tells whether or not an individual variable significantly predicts the dependent variable. You can have a significant model, but a non-significant predictor variable, as shown with Variable 2. Typically, if thepvalue is below .050, the value is considered significant.
Take the course: Linear Regression Analysis
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